2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

一类MIMO系统连续状态空间模型的参数辨识频域方法

鲁兴举 郑志强

鲁兴举, 郑志强. 一类MIMO系统连续状态空间模型的参数辨识频域方法. 自动化学报, 2016, 42(1): 145-153. doi: 10.16383/j.aas.2016.c150150
引用本文: 鲁兴举, 郑志强. 一类MIMO系统连续状态空间模型的参数辨识频域方法. 自动化学报, 2016, 42(1): 145-153. doi: 10.16383/j.aas.2016.c150150
LU Xing-Ju, ZHENG Zhi-Qiang. Identification of Continuous State-space Model Parameters for a Class of MIMO Systems:A Frequency Domain Approach. ACTA AUTOMATICA SINICA, 2016, 42(1): 145-153. doi: 10.16383/j.aas.2016.c150150
Citation: LU Xing-Ju, ZHENG Zhi-Qiang. Identification of Continuous State-space Model Parameters for a Class of MIMO Systems:A Frequency Domain Approach. ACTA AUTOMATICA SINICA, 2016, 42(1): 145-153. doi: 10.16383/j.aas.2016.c150150

一类MIMO系统连续状态空间模型的参数辨识频域方法

doi: 10.16383/j.aas.2016.c150150
基金项目: 

国家自然科学基金 61203095, 61403407

详细信息
    作者简介:

    鲁兴举 国防科技大学机电工程与自动化学院博士研究生.主要研究方向为飞行器制导与控制.E-mail:luxingju@163.com

    通讯作者:

    郑志强 国防科技大学机电工程与自动化学院教授.主要研究方向为飞行器制导与控制,机器人控制.本文通信作者.E-mail:zqzheng@nudt.edu.cn

Identification of Continuous State-space Model Parameters for a Class of MIMO Systems:A Frequency Domain Approach

Funds: 

National Natural Science Foundation of China 61203095, 61403407

More Information
    Author Bio:

    Ph. D. candidate at the College of Mechatronic Engineering and Automation, National University of Defense Technology. His research interest covers aircraft guidance and control

    Corresponding author: ZHENG Zhi-Qiang Professor at the College of Mechatronic Engineering and Automation, National University of Defense Technology. His research interest covers aircraft guidance and control, and robot control. Corresponding author of this paper
  • 摘要: 在连续时间状态空间模型的参数辨识中,针对系统状态微分项获取困难这一问题,对输入、状态及输出序列应用离散傅里叶变换,得到复数域线性回归方程,并给出了不同形式的最小二乘解估计式.以飞行器多输入多输出(Multiple-input multiple-output, MIMO)状态空间模型为例,设计正交多正弦信号对系统进行多通道同时激励,在一次激励的情况下就可以辨识出所有模型参数,从而提高辨识实验效率.仿真实验证明了方法的有效性和结果的准确性.
  • 图  1  正交多正弦信号频谱分布

    Fig.  1  Spectral distribution of orthogonal multi-sine signals

    图  2  正交多正弦信号的时间历程(采样点数= 1000)

    Fig.  2  Plots of orthogonal multi-sine signals (Samples =1000)

    图  3  F-16飞机纵向通道输入及响应时间历程

    Fig.  3  Plots of input and response of F-16 aircraft longitudinal channel

    图  4  系统输入及状态变量的DFT曲线

    Fig.  4  DFT plots of system input and states

    图  5  单输入系统参数辨识结果的误差及其置信区间

    Fig.  5  Error and confidence interval of identification results for a single-input system

    图  6  频域最小二乘算法计算时间

    Fig.  6  Time consumption of frequency domain least-squares algorithm

    图  7  Multi-sine多输入激励及系统响应

    Fig.  7  Multiple multi-sine inputs and system response

    图  8  3-2-1-1多输入激励及系统响应

    Fig.  8  Multiple 3-2-1-1 inputs and system response

    图  9  MIMO 系统参数辨识结果的误差及置信区间

    Fig.  9  Error and con¯dence interval of identi¯cation results for a MIMO system

    表  1  不同频域最小二乘算法的结果比较

    Table  1  Comparison of results with different frequency domain least-square algorithms

    LS algorithm ${\hat{A}_{{\text{lon}}}}$ $\hat{B}_{{\rm{lon}}}$
    LS_Re -0.0161 -3.8292 -1.0548 -32.0137 10.1047
    -0.0003 -0.7514 0.9272 -0.0020 -0.1613
    -0.0004 -4.1575 -1.3190 -0.1572 -14.1069
    0.0009 -0.0693 1.0046 0.0579 0.0139
    LS_Im -0.0175 -3.7048 -1.0633 -32.1205 10.0944
    -0.0002 -0.7543 0.9274 0.0005 -0.1609
    0.0026 -4.3783 -1.3070 0.0307 -14.0746
    0.0019 -0.1118 1.0048 0.0929 0.0298
    LS_EAM -0.0165 -3.7773 -1.0591 -32.0588 10.1050
    -0.0002 -0.7523 0.9273 -0.0012 -0.1613
    0.0002 -4.2252 -1.3142 -0.0985 -14.1047
    0.0010 -0.0655 1.0036 0.0544 0.0161
    下载: 导出CSV
  • [1] Jategaonkar R V. Flight Vehicle System Identification(A Time Domain Methodology). Reston:American Institute of Aeronautics and Astronautics, 2006.
    [2] Pintelon R, Schoukens J. System Identification:A Frequency Domain Approach(2nd Edition). New York:Wiley-IEEE Press, 2012.
    [3] Marelli D, Fu M Y. Exact identification of continuous-time systems from sampled data. In:Proceedings of 2007 IEEE International Conference on Acoustics, Speech and Signal Processing. Honolulu, HI:IEEE, 2007. III-757-III-760
    [4] 李幼凤, 苏宏业, 褚健. 子空间模型辨识方法综述. 化工学报, 2006, 57(3):473-479 http://www.cnki.com.cn/Article/CJFDTOTAL-HGSZ200603000.htm

    Li You-Feng, Su Hong-Ye, Chu Jian. Overview on subspace model identification methods. Journal of Chemical Industry and Engineering(China), 2006, 57(3):473-479 http://www.cnki.com.cn/Article/CJFDTOTAL-HGSZ200603000.htm
    [5] Van Overschee P, De Moor B L. Subspace Identification for Linear Systems:Theory, Implementation, Applications. Dordrecht:Kluwer Academic Publishers, 1996.
    [6] McKelvey T, Akcay H, Ljung L. Subspace-based multivariable system identification from frequency response data. IEEE Transactions on Automatic Control, 1996, 41(7):960-979 http://sites-test.uclouvain.be/socn/www/Documents/lec2a.pdf
    [7] Houtzager I, van Wingerden J, Verhaegen M. Recursive predictor-based subspace identification with application to the real-time closed-loop tracking of flutter. IEEE Transactions on Control Systems Technology, 2012, 20(4):934-949 doi: 10.1109/TCST.2011.2157694
    [8] Verhaegen M, Dewilde P. Subspace model identification Part 1. The output-error state-space model identification class of algorithms. International Journal of Control, 1992, 56(5):1187-1210 doi: 10.1080/00207179208934363
    [9] Verhaegen M, Dewilde P. Subspace model identification Part 2. Analysis of the elementary output-error state-space model identification algorithm. International Journal of Control, 1992, 56(5):1211-1241 doi: 10.1080/00207179208934364
    [10] Verhaegen M. Identification of the deterministic part of MIMO state space models given in innovations form from input-output data. Automatica, 1994, 30(1):61-74 doi: 10.1016/0005-1098(94)90229-1
    [11] Van Overschee P, De Moor B. N4SID:subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica, 1994, 30(1):75-93 doi: 10.1016/0005-1098(94)90230-5
    [12] Larimore W E. Canonical variate analysis in identification, filtering, and adaptive control. In:Proceedings of the 29th IEEE Conference on Decision and Control. Honolulu, HI:IEEE, 1990. 596-604
    [13] Tischler M B, Remple R K[著], 张怡哲, 左军毅[译]. 飞机和旋翼机系统辨识:工程方法和飞行试验案例. 北京:航空工业出版社, 2012.

    Tischler M B, Remple R K[Author], Zhang Yi-Zhe, Zuo Jun-Yi[Translator]. Aircraft and Rotorcraft System Identification——Engineering Methods with Flight Test Examples. Beijing:Aviation Industry Press, 2012.
    [14] Klein V. Maximum Likelihood Method for Estimating Airplane Stability and Control Parameters from Flight Data in the Frequency Domain. NASA Technical Paper 1637, 1980.
    [15] Morelli E A. Real-time parameter estimation in the frequency domain. Journal of Guidance, Control, and Dynamics, 2000, 23(5):812-818 doi: 10.2514/2.4642
    [16] Tischler M B. System identification methods for aircraft flight control development and validation. Advances in Aircraft Flight Control. London:Taylor & Francis, 1995. 35-69
    [17] Haverkamp B R J, Chou C T, Verhaegen M, Johansson R. Identification of continuous-time MIMO state space models from sampled data, in the presence of process and measurement noise. In:Proceedings of the 35th IEEE Conference on Decision and Control. Kobe:IEEE, 1996. 1539-1544
    [18] Sinha N K. Identification of continuous-time systems from samples of input-output data:an introduction. Sadhana, 2000, 25(2):75-83 doi: 10.1007/BF02703750
    [19] Rao G P, Unbehauen H. Identification of continuous-time systems. IEE Proceedings——Control Theory and Applications, 2006, 153(2):185-220 doi: 10.1049/ip-cta:20045250
    [20] Olofsson B, Sornmo O, Robertsson A, Johansson R. Continuous-time gray-box identification of mechanical systems using subspace-based identification methods. In:Proceedings of the 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics(AIM). Besacon:IEEE, 2014. 327-333
    [21] 丁锋. 系统辨识新论. 北京:科学出版社, 2013.

    Ding Feng. System Identification:New Theory and Methods. Beijing:Science Press, 2013.
    [22] 梅长林, 王宁. 近代回归分析方法. 北京:科学出版社, 2012.

    Mei Chang-Lin, Wang Ning. Modern Regression Analysis. Beijing:Science Press, 2012.
    [23] McGrail A K. OnBoard Parameter Identification for a Small UAV[Ph.D. dissertation]. West Virginia University, USA, 2012.
    [24] 吴密霞, 刘春玲. 多元统计分析. 北京:科学出版社, 2014.

    Wu Mi-Xia, Liu Chun-Ling. Multivariate Statistical Analysis. Beijing:Science Press, 2014.
    [25] 王桂松, 史建红, 尹素菊, 吴密霞. 线性模型引论. 北京:科学出版社, 2004.

    Wang Gui-Song, Shi Jian-Hong, Yin Su-Ju, Wu Mi-Xia. Introduction to Linear Model. Beijing:Science Press, 2004.
    [26] Morelli E A. Flight-test experiment design for characterizing stability and control of hypersonic vehicles. Journal of Guidance, Control, and Dynamics, 2009, 32(3):949-959 doi: 10.2514/1.37092
    [27] Morelli E A. Flight test maneuvers for efficient aerodynamic modeling. Journal of Aircraft, 2012, 49(6):1857-1867 doi: 10.2514/1.C031699
    [28] Russell R S. Non-linear F-16 simulation using Simulink and Matlab. Technique Paper, University of Minnesota, 2003.
    [29] Phillips K, Gururajan S, Campa G, Seanor B, Gu Y, Merceruio Z, Napolitano M R. Nonlinear aircraft model identification and validation for a fault-tolerant flight control system. In:Proceedings of the 2010 AIAA Atmospheric Flight Mechanics Conference. Toronto, Canada:AIAA, 2010.
  • 加载中
图(9) / 表(1)
计量
  • 文章访问数:  2025
  • HTML全文浏览量:  240
  • PDF下载量:  1265
  • 被引次数: 0
出版历程
  • 收稿日期:  2015-03-31
  • 录用日期:  2015-09-06
  • 刊出日期:  2016-01-01

目录

    /

    返回文章
    返回